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The manipulator arm design is generally related to a search for a compromise between conflicting requirements. Finding this compromise requires the resolution of a bi-level optimization problem (the respect of some specific constraints involves solving other optimization sub-problems). In this paper, we propose a novel approach for the optimal dimensioning of a two-degrees-of-freedom manipulator arm, where the objective function is defined by the minimization of the structure’s overall mass. The constraints that are taken into consideration can be divided into three categories: geometrical (bounds on design variables, admissible limit of the static deflection at the endpoint of the effector), kinematic (bounds on joint velocities and the accelerations of the actuators), and dynamic (admissible limit of the dynamic deflection at the endpoint of the effector). To achieve the penalizing dynamic configurations in terms of actuator torques and dynamic deflection, a quasi-static deformation model and a recurrent dynamic model were used. The static and dynamic deflection constraints are treated as optimization sub-problems, where their outcomes are integrated, at each iteration, into the global optimization process. The validation of the developed quasi-static deformation model as well as the proposed optimal dimensioning approach is conducted by comparing results with Ansys software simulations. Notably, The proposed approach demonstrates a significantly faster computation time compared to the Ansys software (06 s for the proposed approach vs 21 h for Ansys). Even though Ansys software performs optimal dimensioning for an imposed trajectory.

The Near-Surface Mounted (NSM) strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent years. Over the past two decades, researchers have extensively studied its potential, advantages, and applications, as well as related parameters, aiming at optimization of construction systems. However, there is still a need to explore further, both from a static perspective, which involves accounting for the non-conservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer (FRP) rods and resin and is typically neglected by existing analytical models, as well as from a dynamic standpoint, which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of reinforcement. To address this gap in knowledge, this research involves static and dynamic tests on simply supported reinforced concrete (RC) beams using rods of NSM carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP). The main objective is to examine the effects of various strengthening methods. This research conducts bending tests with loading cycles until failure, and it helps to define the behavior of beam specimens under various damage degrees, including concrete cracking. Dynamic analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading process. In addition, application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed to optimize Gradient Boosting (GB) training performance for concrete strain prediction in NSM-FRP RC. The GB using Particle Swarm Optimization (GBPSO) and GB using Genetic Algorithm (GBGA) systems were trained using an experimental data set, where the input data was a static applied load and the output data was the consequent strain. Hybrid models of GBPSO and GBGA have been shown to provide highly accurate results for predicting strain. These models combine the strengths of both optimization techniques to create a powerful and efficient predictive tool.

Recently, great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties. Much effort has been made in experimental studies to advance the understanding of geopolymer concrete, in which compressive strength is one of the most important properties. To facilitate engineering work on the material, an efficient predicting model is needed. In this study, three machine learning (ML)-based models, namely deep neural network (DNN), K-nearest neighbors (KNN), and support vector machines (SVM), are developed for forecasting the compressive strength of the geopolymer concrete. A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models. A careful procedure for data preprocessing is implemented, by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process. The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed, thus the generalizability of the models is ensured. The effectiveness of the models is assessed via statistical metrics including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and the recently proposed performance index (PI). The basic mean square error (MSE) is used as the loss function to be minimized during the model fitting process. The three ML-based models are successfully developed for estimating the compressive strength, for which good correlations between the predicted and the true values are obtained for DNN, KNN, and SVM. The numerical results suggest that the DNN model generally outperforms the other two models.

In this paper, the Concrete Damaged Plastic (CDP) model in the commercial Finite Element software ABAQUS is proposed. The main goal is to capture the stress–strain relationship in both compressive and tensile behavior. In the CDP model, determining damage variables is one of the important issues and needs to be considered. Instead of relying on values from previous studies, the damage variants in compression (dc) and tension (dt) are proposed and expressed by hyperbolic tangent functions. Particularly, the damage variables are adjusted by examine the coefficients αin compression and βin tension behaviors that most closely match the experiment. To demonstrate the reliability and effectiveness of the proposed model, numerical simulations are performed. Specifically, numerical simulations of high-strength concrete C60, C80, and C110 are conducted. A comparison is made between the simulation of the developed model and experimental data to evaluate the performance of the model. Additionally, to boost the model’s dependability, the paper includes two more instances of reinforced concrete slabs and axially compressed CFT columns. Consequently, the experiment findings and the simulation results derived from the examples show a reasonable degree of consistency. It is demonstrated that the CDP model presented in this research is extremely dependable and can be applied to the simulation of intricate concrete structures.

In this paper, for the first time, a variable velocity strategy particle swarm optimization (VVS-PSO) is presented to solve the optimization problems ranging from numerical functions to real-world problems. VVS-PSO introduces a new term added in the velocity updating process at each iteration. This new term is controlled by a reduction linear function, which allows VVS-PSO to reach a faster convergence rate. At the same time, it also leads to enhance the accuracy level. In this way, the strategy of position updating in VVS-PSO is more flexible than that of the original PSO. This strategy will support VVS-PSO to improve the distance between the current step and the previous step and to expand the feasible search space around each particle. To illustrate the convergence rate and level of accuracy of VVS-PSO, the original PSO and 4 well-known optimization algorithms are employed to solve 23 classical benchmark functions. Then, an engineering design problem and experimental validation using a four-storey steel frame are also presented to examine the reliability of VVS-PSO for solving particular real applications. VVS-PSO finally is applied to a real 3D reinforced concrete structure for the purpose of damage assessment. First, the modal assurance criterion (MAC) method, which considers the differences between the mode shapes, is combined with the Root-Mean-Square-Error (RMSE) that registers the differences between frequencies at two states, e.g., damaged and undamaged structures, to determine the objective function. Then, VVS-PSO is used to minimize the objective function, which accounts for variables related to stiffness reduction in elements. The presented results illustrate that VVS-PSO can solve the optimization and structural damage assessment problems with very high accuracy and reliability.

Structural health monitoring (SHM) and Non-destructive Damage Identification (NDI) using responses of structures under dynamic excitation have an imperative role in the engineering application to make the structures safe. Interpretations of structural responses known as inverse problems are emerging topics with a large body of works in the literature. They have been widely solved with Machine Learning (ML) techniques such as Artificial Neural Network (ANN), Deep Neural Network (DNN), Adaptive Network-based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). Nonetheless, these approaches can precisely predict the inverse problems of civil structures (e.g., truss or frame systems) with low damage levels, which have to wait until the structures reach certain damage or deteriorate level. The issue is related to the fact that most of the real structures have very low damage levels during their routine maintenances and usually be neglected due to limitations of the current techniques. This paper proposes a combination of Particle Swarm Optimization and Support Vector Machine (PSO-SVM) for damage identifications. The proposed approach is inspired by the effective searching capability of PSO, which can eliminate the redundant input parameters and robust SVM technique to classify damage locations effectively. In other words, natural frequencies and mode shapes extracted from the numerical examples of truss and frame structures are used as input parameters in which the redundant parameters might lead to reduction of the accuracy in the predicting models. The proposed PSO-SVM shows superior accuracy prediction in both damage locations and damage levels compared to the other ML models. It also substantially outperforms other ML models through validated cases of low damage levels.

Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

In this paper, damage detection, localization and quantification are performed using modal strain energy change ratio (MSEcr) as damage indicator combined with a new optimization technique, namely slime mould algorithm (SMA) developed in 2020. The SMA algorithm is employed to assess structural damage and monitor structural health. Two structures, including a laboratory beam and a bar planar truss are considered to study the effectiveness of the proposed approach. Another recent algorithm called marine predators algorithm (MPA) is also used for comparison purposes with SMA. The MSEcr is utilized in the first stage to predict the location of the damaged elements. Single and multiple damages cases are analysed based on different number of modes to study the sensitivity of the proposed indicator to the total number of modes considered in the analysis. Next, this indicator is used as an objective function in a second stage to solve the inverse problem using SMA and MPA for damage quantification of the elements identified in the first stage. Experimental validation is conducted using a 3D frame structure with four stories that have damaged components. It is demonstrated that the proposed approach, using MSEcr and SMA, provides superior results for the considered structures. The effectiveness of this technique is tested by introducing a white Gaussian noise with different levels, namely 2% and 4%. The results show that the provided approach can predict the location and level of damage with high accuracy after introducing the noise.

ABSTRACTDetecting and locating damage is a crucial endeavor within the field of structural integrity. While Artificial Neural Networks (ANNs) have shown promise in this regard, they have certain limitations that can be overcome through modifications in terms of their structural design and training methodologies. In this study, we propose a new optimization approach, specifically leveraging the Grasshopper Optimization Algorithm (GOA), to enhance the performance of ANNs for predicting multiple damages represented by holes in the aluminum plate. Input parameters are derived from natural frequencies, while hole locations serve as outputs. We utilize a Finite Element Model (FEM) to generate data through simulation, varying hole locations for comprehensive analysis. To authenticate our method, we gather experimental data from vibration analyses of damaged plates spanning various hole locations. A comparative analysis is conducted of proposed algorithm by evaluating its performance against two established metaheuristic algorithms: the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). This comparison was performed to assess the relative effectiveness of our approach. Our novel approach demonstrates superior performance in damage forecasting, offering promising prospects for structural integrity applications.

The static bending behavior of porous functionally graded (PFG) micro-plate under the geometrically nonlinear analysis is studied in this article. A small-scale nonlinear solution is established using the Von-Kármán hypothesis and the modified couple stress theory (MCST). To obtain the deflection of the plate, the Reddy higher-order plate theory coupled with isogeometric analysis (IGA) is utilized. The distribution of porosities is assumed to be even and uneven across the plate’s thickness and the effective material properties of porous functionally graded micro-plate are calculated using the refined rule-of-mixture hypothesis. The influence of power index, porosity parameter and material length scale parameter on the nonlinear behaviors of static bending of porous FGM micro-plates are also investigated using several numerical examples.

This paper addresses the issue of the latest analytical method when tackling a vacuum consolidation problem in a ground improvement project using prefabricated vertical drains (PVD) and the necessity of using numerical simulation. This paper aims to introduce a matching scheme to derive appropriate soil and drain properties in analytical solution and numerical modeling (axisymmetric and plane strain conditions). The scheme started with the analytical solution with a trial process of matching the predicted and measured settlements. The derived parameters are then adopted in the numerical modeling. Depending on the scenarios considered including axisymmetric and plane strain conditions, some parameters such as permeability need to be adjusted to have the best fit in the settlement estimation. In situ monitoring data from a case history of a road construction project in Vietnam was adopted in the back-analysis to demonstrate the application of the proposed matching scheme. In addition, the effectiveness of soft ground treatment by prefabricated vertical drain is evaluated in numerical modeling and field measurement. Very good agreement in settlement behavior and the effective stress variation between numerical simulation and field measurement have illustrated the proposed matching scheme’s appropriateness.

Purpose: Although damage's location detection by Wavelet Transforms (WTs) is quantitive, damage's level detection is qualitative. It is a weakness of wavelet transform that cannot detect the level of damages quantitatively. For overcoming this weakness, this paper proposes a novel robust approach for quantifying the damage level of rectangular laminated composite plates using a combination of wavelet transforms and Artificial Neural Networks (WT-ANN). Methods: The finite element method generates two-dimensional signals of vibration amplitudes in the rectangular laminated composite plates. Next, a two-dimensional WTs is applied to the signals to generate wavelet coefficients and wavelet modulus. An artificial neural network is developed to quantify damage's level based on damage locations detected by wavelet transform and fundamental natural frequency. Results: Findings demonstrate that it is possible to quantify damages in rectangular laminated composite plates with high accuracy (R= 0.992). A disturbing phenomenon in damage identification techniques based on wavelet transform is discovered and called “edge noise”. Conclusion: Results show that the proposed WT-ANN technique can detect all single-damage scenarios with very high accuracy by eliminating edge noises.

Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN—PSO and DNN—GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter (PIλDND2N2) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based PIλDND2N2controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a PIλDND2N2controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

Vibration-based damage detection tools are frequently employed because of their advantages over other non-destructive techniques. This paper presents an improved Frequency Response Function (FRF) indicator for damage identification in complex structures. To verify the effectiveness of the improved damage indicator, different structures are used, namely a 20-Bar Planar Truss (2D), a 28-Bar Space Truss (3D), a 72-Bar Space Truss, and a 600-bar Space Truss. The numerical models of all these structures are built in MATLAB using Finite Element Method (FEM). The improved indicator detects and localises single and multiple damages in the first stage. Next, after eliminating the healthy elements, it is used in a new objective function to solve the damage quantification problem using recent optimization techniques, including Artificial Gorilla Troops Optimizer (GTO), Dingo Optimization Algorithm (DOA), African Vulture Optimization algorithm (AVOA), and Gradient-Based Optimizer (GBO). The results demonstrate that all optimization techniques can accurately predict the exact level of damage. However, GTO is the most efficient in terms of convergence. To study the effectiveness of this indicator in case of noisy data, different levels of noise are considered in the damage assessment exercises.